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NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision

NanoMan：自我优化纳米级制造平台，实现多尺度精度

基本信息

批准号：
EP/V055089/1
负责人：
Nicholas Warren
金额：
$ 182.5万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV055089%2F1
关键词：
NanoMan Self Optimising Nanoscale Manufacturing

项目摘要

Improving our current lifestyle and ensuring health of a growing population is reliant on the development of more advanced consumer products. Many of these engineered products have advanced functionality delivered by particles with nanometre dimensions, many thousands of times smaller than the width of a human hair. The exact size of these nanoparticles determines the mechanism of action and performance for the specific application. In healthcare, many drugs require encapsulation within polymer nanoparticles for several reasons, including for dissolving insoluble drugs, protecting drugs from unwanted degradation (e.g. mRNA vaccines) and providing efficient delivery (anti-cancer drugs). In electronics, the colour and intensity of light produced can be finely tuned by controlling the size of quantum dot nanoparticles, thus resulting in much higher quality displays, ultra-thin smart coatings (e.g. for wearable technologies), advanced diagnostics, high intensity medical imaging or high efficiency solar panels. The accuracy required to produce these materials is phenomenal and often only achieved reproducibly in dedicated research laboratories by specialist scientists. There has therefore been little progress on scaling up in a cost-effective or sustainable manner. In this project we will build platform technologies, comprising advanced chemical reactors underpinned by computational intelligence, which can scale up production of advanced nanoparticle products without loss in the precise control over structural dimensions which are achieved in research laboratories. We will build laboratory reactors which can be programmed to monitor the nanoparticle formation process in real time and relate conditions to the particle properties. Throughout the manufacturing process the machine learning algorithms will direct the reactors towards achieving the desired specification through 'self-optimisation' of conditions. A critical part of the project is then using the data obtained in the lab experiments to build a relationship between process and product which can be transferred onto equipment which can make the materials on a commercially relevant scale in a process called augmented lossless scale-up. We will take the optimised laboratory nanoparticle formation processes and demonstrate scale in several manufacturing environments, including R&D process laboratories and Commercial manufacturing facilities at our partners sites. Such demonstration will encourage further innovation beyond the lifetime of the project which can work towards realising advanced materials currently confined to research laboratories.

改善我们目前的生活方式，确保不断增长的人口的健康，有赖于开发更先进的消费产品。这些工程产品中的许多都具有先进的功能，这些功能是由纳米尺寸的颗粒提供的，这些颗粒的尺寸比人类头发的宽度小几千倍。这些纳米颗粒的确切大小决定了特定应用的作用机制和性能。在医疗保健中，许多药物需要在聚合物纳米颗粒中包裹，原因有几个，包括溶解不溶的药物，保护药物免受不必要的降解(例如，信使核糖核酸疫苗)和提供有效的递送(抗癌药物)。在电子领域，可以通过控制量子点纳米颗粒的大小来微调产生的光的颜色和强度，从而实现更高质量的显示器、超薄智能涂层(例如用于可穿戴技术)、先进诊断、高强度医学成像或高效太阳能电池板。生产这些材料所需的精确度是惊人的，而且通常只有在专门的研究实验室中由专业科学家才能重复实现。因此，在以具有成本效益或可持续的方式扩大规模方面进展甚微。在这个项目中，我们将建立平台技术，包括由计算智能支持的先进化学反应器，这些技术可以扩大先进纳米颗粒产品的生产，而不会损失研究实验室实现的对结构尺寸的精确控制。我们将建造实验室反应堆，可以对其进行编程，以实时监控纳米颗粒的形成过程，并将条件与颗粒特性相关联。在整个制造过程中，机器学习算法将引导反应堆通过条件的“自我优化”达到所需的规格。该项目的一个关键部分是使用实验室实验中获得的数据来建立过程和产品之间的关系，这些关系可以转移到设备上，使材料在一个称为增强无损放大的过程中达到商业上的相关规模。我们将采用优化的实验室纳米颗粒形成过程，并在几个制造环境中展示规模，包括我们合作伙伴地点的研发过程实验室和商业制造设施。这样的演示将鼓励项目生命周期之后的进一步创新，这可以努力实现目前仅限于研究实验室的先进材料。